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A data mining approach to forecast behavior

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  • Nihat Altintas
  • Michael Trick

Abstract

This study presents a data mining analysis of forecasting patterns in a supply chain. Multiple customers who are auto manufacturers order from a large auto parts supplier. The auto manufacturers provide forecasts for future orders and update them before the due date. The supplier uses these forecasts to plan production in advance. The accuracy of the forecasts varies from customer to customer. We provide a framework to analyze the forecast performance of the customers. There are different complexities in forecasts that are captured from our data set. Daily flow analysis helps to transform data and obtain accuracy ratios of forecasts. Customers are then classified based on their forecast performances. We demonstrate the application of some recent developments in clustering and pattern recognition analysis to performance analysis of customers. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Nihat Altintas & Michael Trick, 2014. "A data mining approach to forecast behavior," Annals of Operations Research, Springer, vol. 216(1), pages 3-22, May.
    • Handle: RePEc:spr:annopr:v:216:y:2014:i:1:p:3-22:10.1007/s10479-012-1236-9
    DOI: 10.1007/s10479-012-1236-9
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    References listed on IDEAS

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    Cited by:

    1. Asil Oztekin, 2018. "Creating a marketing strategy in healthcare industry: a holistic data analytic approach," Annals of Operations Research, Springer, vol. 270(1), pages 361-382, November.
    2. Benjamin T. Hazen & Joseph B. Skipper & Christopher A. Boone & Raymond R. Hill, 2018. "Back in business: operations research in support of big data analytics for operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 201-211, November.
    3. John A. Aloysius & Hartmut Hoehle & Soheil Goodarzi & Viswanath Venkatesh, 2018. "Big data initiatives in retail environments: Linking service process perceptions to shopping outcomes," Annals of Operations Research, Springer, vol. 270(1), pages 25-51, November.

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